IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i14p7592-d595750.html
   My bibliography  Save this article

Which National Factors Are Most Influential in the Spread of COVID-19?

Author

Listed:
  • Hakyong Kim

    (Department of Industrial Engineering, Seoul National University, Seoul 08826, Korea
    These authors contributed equally to this work as co-first authors.)

  • Catherine Apio

    (Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
    These authors contributed equally to this work as co-first authors.)

  • Yeonghyeon Ko

    (Department of Statistics, Seoul National University, Seoul 08826, Korea
    Department of Archeology and Art History, Seoul National University, Seoul 08826, Korea
    These authors contributed equally to this work as co-first authors.)

  • Kyulhee Han

    (Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea)

  • Taewan Goo

    (Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea)

  • Gyujin Heo

    (Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea)

  • Taehyun Kim

    (Department of Statistics, Seoul National University, Seoul 08826, Korea)

  • Hye Won Chung

    (Department of Chemistry, Seoul National University, Seoul 08826, Korea)

  • Doeun Lee

    (Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea)

  • Jisun Lim

    (The Research Institute of Basic Sciences, Seoul National University, Seoul 08826, Korea)

  • Taesung Park

    (Department of Statistics, Seoul National University, Seoul 08826, Korea)

Abstract

The outbreak of the novel COVID-19, declared a global pandemic by WHO, is the most serious public health threat seen in terms of respiratory viruses since the 1918 H1N1 influenza pandemic. It is surprising that the total number of COVID-19 confirmed cases and the number of deaths has varied greatly across countries. Such great variations are caused by age population, health conditions, travel, economy, and environmental factors. Here, we investigated which national factors (life expectancy, aging index, human development index, percentage of malnourished people in the population, extreme poverty, economic ability, health policy, population, age distributions, etc.) influenced the spread of COVID-19 through systematic statistical analysis. First, we employed segmented growth curve models (GCMs) to model the cumulative confirmed cases for 134 countries from 1 January to 31 August 2020 (logistic and Gompertz). Thus, each country’s COVID-19 spread pattern was summarized into three growth-curve model parameters. Secondly, we investigated the relationship of selected 31 national factors (from KOSIS and Our World in Data ) to these GCM parameters. Our analysis showed that with time, the parameters were influenced by different factors; for example, the parameter related to the maximum number of predicted cumulative confirmed cases was greatly influenced by the total population size, as expected. The other parameter related to the rate of spread of COVID-19 was influenced by aging index, cardiovascular death rate, extreme poverty, median age, percentage of population aged 65 or 70 and older, and so forth. We hope that with their consideration of a country’s resources and population dynamics that our results will help in making informed decisions with the most impact against similar infectious diseases.

Suggested Citation

  • Hakyong Kim & Catherine Apio & Yeonghyeon Ko & Kyulhee Han & Taewan Goo & Gyujin Heo & Taehyun Kim & Hye Won Chung & Doeun Lee & Jisun Lim & Taesung Park, 2021. "Which National Factors Are Most Influential in the Spread of COVID-19?," IJERPH, MDPI, vol. 18(14), pages 1-22, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7592-:d:595750
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/14/7592/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/14/7592/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kofi Annan, 2018. "Data can help to end malnutrition across Africa," Nature, Nature, vol. 555(7694), pages 7-7, March.
    2. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Shima Hamidi & Sadegh Sabouri & Reid Ewing, 2020. "Does Density Aggravate the COVID-19 Pandemic?," Journal of the American Planning Association, Taylor & Francis Journals, vol. 86(4), pages 495-509, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrés Rodríguez‐Pose & Chiara Burlina, 2021. "Institutions and the uneven geography of the first wave of the COVID‐19 pandemic," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 728-752, September.
    2. Mehmet Ronael & Tüzin Baycan, 2022. "Place-based factors affecting COVID-19 incidences in Turkey," Asia-Pacific Journal of Regional Science, Springer, vol. 6(3), pages 1053-1086, October.
    3. Gayatri Kawlra & Kazuki Sakamoto, 2023. "Spatialising urban health vulnerability: An analysis of NYC’s critical infrastructure during COVID-19," Urban Studies, Urban Studies Journal Limited, vol. 60(9), pages 1629-1649, July.
    4. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    5. Singhal, Amit & Singh, Pushpendra & Lall, Brejesh & Joshi, Shiv Dutt, 2020. "Modeling and prediction of COVID-19 pandemic using Gaussian mixture model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    6. Richard Florida & Andrés Rodríguez-Pose & Michael Storper, 2023. "Critical Commentary: Cities in a post-COVID world," Urban Studies, Urban Studies Journal Limited, vol. 60(8), pages 1509-1531, June.
    7. Fernando Gil-Alonso & Jordi Bayona-i-Carrasco & Isabel Pujadas-Rúbies, 2023. "Is Spanish depopulation irreversible? Recent demographic and spatial changes in small municipalities," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 21(1), pages 277-309.
    8. X. Angela Yao & Andrew Crooks & Bin Jiang & Jukka Krisp & Xintao Liu & Haosheng Huang, 2023. "An overview of urban analytical approaches to combating the Covid-19 pandemic," Environment and Planning B, , vol. 50(5), pages 1133-1143, June.
    9. Shuai Yu & Bin Li & Dongmei Liu, 2023. "Exploring the Public Health of Travel Behaviors in High-Speed Railway Environment during the COVID-19 Pandemic from the Perspective of Trip Chain: A Case Study of Beijing–Tianjin–Hebei Urban Agglomera," IJERPH, MDPI, vol. 20(2), pages 1-22, January.
    10. Mattia Bertin, 2022. "Lessons to be learnt from COVID-19," Briefs, Fondazione Eni Enrico Mattei, October.
    11. Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
    12. Sui Zhang & Minghao Wang & Zhao Yang & Baolei Zhang, 2021. "A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective," IJERPH, MDPI, vol. 18(24), pages 1-16, December.
    13. Richard Florida & Charlotta Mellander, 2022. "The geography of COVID-19 in Sweden," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(1), pages 125-150, February.
    14. Ellen, Ingrid Gould & Howland, Renata & Glied, Sherry, 2023. "Demons of density do higher-density environments put people at greater risk of contagious disease?," Journal of Housing Economics, Elsevier, vol. 59(PB).
    15. Amaral, Marco A. & Oliveira, Marcelo M. de & Javarone, Marco A., 2021. "An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    16. Nadia Yusuf & Lamia Saud Shesha, 2021. "Economic Role of Population Density during Pandemics—A Comparative Analysis of Saudi Arabia and China," IJERPH, MDPI, vol. 18(8), pages 1-18, April.
    17. Chenghe Guan & Junjie Tan & Brian Hall & Chao Liu & Ying Li & Zhichang Cai, 2022. "The Effect of the Built Environment on the COVID-19 Pandemic at the Initial Stage: A County-Level Study of the USA," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    18. Gerritse, Michiel, 2022. "COVID-19 transmission in cities," European Economic Review, Elsevier, vol. 150(C).
    19. Swapnarekha, H. & Behera, Himansu Sekhar & Nayak, Janmenjoy & Naik, Bighnaraj, 2020. "Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    20. Huang, Chiou-Jye & Shen, Yamin & Kuo, Ping-Huan & Chen, Yung-Hsiang, 2022. "Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7592-:d:595750. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.